Wireless Communication Networks

import numpy as np import matplotlib.pyplot as plt from matplotlib import style style.use('bmh') #Not to show warning messages (to keep the notebook clean) import warnings warnings.filterwarnings('ignore')

#3. Analyze a queue system similar to the tutorial considering 𝜆=3 and 𝜇=4 for 2000 time periods. #time periods size=2000 #Arrival mean=3 arrival = np.random.poisson(mean, size) #Service mean=4 service = np.random.poisson(mean, size) # queue = np.zeros(size+1) for i in range(size): if ((arrival[i] - service[i]) + queue[i]) > 0: queue[i+1] = max(0,(arrival[i] - service[i]) + queue[i]) plt.figure(figsize=(14,5)) #plt.plot( arrival, '--o',label='Arrival') #plt.plot( service, '-x',label='Sevice') plt.plot( queue, 'ok') plt.xlabel('$n$') plt.ylabel('Size') plt.title('Queue') #plt.ylim([0, 1]) #plt.xlim([0, 10]) plt.grid(True) #grid plt.show() print('Worst delay:', np.max(queue)) print('Ratio of Idle time:',( size-np.count_nonzero(queue)/size))

#4. Analyze a queue system similar to the tutorial considering 𝜆=4 and 𝜇=3 for 2000 time periods. #time periods size=10000 #Arrival mean=4 arrival = np.random.poisson(mean, size) #Service mean=3 service = np.random.poisson(mean, size) # queue = np.zeros(size+1) for i in range(size): if ((arrival[i] - service[i]) + queue[i]) > 0: queue[i+1] = max(0,(arrival[i] - service[i]) + queue[i]) plt.figure(figsize=(14,5)) #plt.plot( arrival, '--o',label='Arrival') #plt.plot( service, '-x',label='Sevice') plt.plot( queue, 'ok') plt.xlabel('$n$') plt.ylabel('Size') plt.title('Queue') #plt.ylim([0, 1]) #plt.xlim([0, 10]) plt.grid(True) #grid plt.show() print('Worst delay:', np.max(queue)) print('Ratio of Idle time:',( size-np.count_nonzero(queue)/size))

#5. Analyze a queue system similar to the tutorial considering 𝜆=3 and 𝜇=3 for 2000 time periods. #time periods size=50000 #Arrival mean=3 arrival = np.random.poisson(mean, size) #Service mean=3 service = np.random.poisson(mean, size) # queue = np.zeros(size+1) for i in range(size): if ((arrival[i] - service[i]) + queue[i]) > 0: queue[i+1] = max(0,(arrival[i] - service[i]) + queue[i]) plt.figure(figsize=(14,5)) #plt.plot( arrival, '--o',label='Arrival') #plt.plot( service, '-x',label='Sevice') plt.plot( queue, 'ok') plt.xlabel('$n$') plt.ylabel('Size') plt.title('Queue') #plt.ylim([0, 1]) #plt.xlim([0, 10]) plt.grid(True) #grid plt.show() print('Worst delay:', np.max(queue)) print('Ratio of Idle time:',( size-np.count_nonzero(queue)/size))